{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "替换后的文本： 张北个人网页的网址为http://abc138qaz，喜欢语文、数学和科学。\n",
      "提取的科目： ['数学', '科学']\n",
      "替换数字后的文本： 张三个人网页的网址为http://abc***qaz，喜欢语文、数学和科学。\n",
      "匹配的字符串： ['http://abc']\n"
     ]
    }
   ],
   "source": [
    "#任务一\n",
    "import re\n",
    "\n",
    "text = \"张三个人网页的网址为http://abc138qaz，喜欢语文、数学和科学。\"\n",
    "#(1)用自己的名字替换掉张三；\n",
    "new_text = re.sub(r'张三', '张北', text)\n",
    "print(\"替换后的文本：\", new_text)\n",
    "#（2）输出数学和科学科目名称\n",
    "subjects = re.findall(r'(数学|科学)', text)\n",
    "print(\"提取的科目：\", subjects)\n",
    "#(3)将文本中的所有数字替换为*\n",
    "text_with_stars = re.sub(r'\\d', '*', text)\n",
    "print(\"替换数字后的文本：\", text_with_stars)\n",
    "#(4)输出以h开头，以c为结尾，中间可以有任意多个字符\n",
    "matches = re.findall(r'h.*c', text)\n",
    "print(\"匹配的字符串：\", matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "提取的关键词: ['海洋', '108kW', '20', '盐差', '潮汐能']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#任务二\n",
    "import jieba\n",
    "import jieba.analyse\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "import imageio\n",
    "f = open(r'data/sea.txt', encoding='utf-8')\n",
    "t = f.read()\n",
    "f.close()\n",
    "words = jieba.lcut(t)\n",
    "txt = ' '.join(words)\n",
    "\n",
    "# (1)调用jieba.analyse.extract_tags()函数实现提取文段前5的关键词。\n",
    "keywords = jieba.analyse.extract_tags(t, topK=5, withWeight=False)\n",
    "print(\"提取的关键词:\", keywords)\n",
    "image = imageio.imread(r'data/horse.png')\n",
    "w = WordCloud(font_path=r'data/FZFSJW.ttf', background_color='white', mask=image)\n",
    "w.generate(txt)\n",
    "plt.imshow(w, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "与'文化'相似度最高的10个词: \n",
      "[('地理', 0.7110191583633423), ('交流', 0.6648808121681213), ('素养', 0.653809666633606), ('现代', 0.6447321176528931), ('哲学', 0.6426615715026855), ('艺术', 0.6410525441169739), ('娱乐', 0.6389725208282471), ('民族', 0.6260854005813599), ('交流史', 0.6237207651138306), ('民间', 0.6199003458023071)]\n"
     ]
    }
   ],
   "source": [
    "#第三题\n",
    "import numpy\n",
    "import gensim\n",
    "import numpy as np\n",
    "from jieba import analyse\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models.word2vec import LineSentence\n",
    "\n",
    "def train_word2vec():\n",
    "    cor_data=open('data/TrainData.txt','r',encoding='utf-8')\n",
    "    model=Word2Vec(LineSentence(cor_data),sg=0,vector_size=200,window=5,min_count=5,workers=9)\n",
    "    model.save('model_word2vec')\n",
    "    print(\"与'文化'相似度最高的10个词: \")\n",
    "    print(model.wv.most_similar('文化',topn=10))\n",
    "train_word2vec()\n",
    "def keyword(data):\n",
    "    tfidf=analyse.extract_tags\n",
    "    keywords=tfidf(data)\n",
    "    return keywords\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_keywords(docpath,savepath):\n",
    "    with open(docpath, 'r', encoding='utf-8') as docf, open(savepath,'w') as outf:\n",
    "        for data in docf:\n",
    "            data=data[:len(data)-1]\n",
    "            keywords=keyword(data)\n",
    "            for word in keywords:\n",
    "                outf.write(word + ' ')\n",
    "            outf.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pos(string,char):\n",
    "    space_pos=[]\n",
    "    try:\n",
    "        space_pos=list(((pos) for pos, val in enumerate(string)if(val == char)))\n",
    "    except:\n",
    "        pass\n",
    "    return space_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vector(file_name,model):\n",
    "    with open(file_name,'r') as f:\n",
    "        wordvec_size=200\n",
    "        word_vector=numpy.zeros(wordvec_size)\n",
    "        for data in f:\n",
    "            space_pos=get_pos(data,' ')\n",
    "            first_word=data[0:space_pos[0]]\n",
    "            if model.wv.__contains__(first_word):\n",
    "                word_vector=word_vector + model.wv[first_word]\n",
    "            for i in range(len(space_pos)-1):\n",
    "                word=data[space_pos[i]:space_pos[i+1]]\n",
    "                if model.wv.__contains__(word):\n",
    "                    word_vector=word_vector+model.wv[word]\n",
    "        return word_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.742 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zuoye1和zuoye2的相似度为: 0.5206\n"
     ]
    }
   ],
   "source": [
    "def similarity(vector1,vector2):\n",
    "    vector1_abs=np.sqrt(vector1.dot(vector1))\n",
    "    vector2_abs=np.sqrt(vector2.dot(vector2))\n",
    "    if vector2_abs != 0 and vector1_abs != 0:\n",
    "        similarity = (vector1.dot(vector2))/(vector1_abs * vector2_abs)\n",
    "    else:\n",
    "        similarity = 0\n",
    "    return similarity\n",
    "def main():\n",
    "    # 加载训练好的Word2Vec模型\n",
    "    model = Word2Vec.load('model_word2vec')\n",
    "    \n",
    "    # 假设zuoye1和zuoye2是两个文本文件路径\n",
    "    zuoye1_path = 'data/zuoye1.txt'\n",
    "    zuoye2_path = 'data/zuoye2.txt'\n",
    "    \n",
    "    # 提取关键词并保存到临时文件\n",
    "    zuoye1_keywords_path = 'data/zuoye1_keywords.txt'\n",
    "    zuoye2_keywords_path = 'data/zuoye2_keywords.txt'\n",
    "    \n",
    "    get_keywords(zuoye1_path, zuoye1_keywords_path)\n",
    "    get_keywords(zuoye2_path, zuoye2_keywords_path)\n",
    "    \n",
    "    # 获取关键词向量\n",
    "    vector1 = get_vector(zuoye1_keywords_path, model)\n",
    "    vector2 = get_vector(zuoye2_keywords_path, model)\n",
    "    \n",
    "    # 计算相似度\n",
    "    sim = similarity(vector1, vector2)\n",
    "    print(f\"zuoye1和zuoye2的相似度为: {sim:.4f}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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